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#include "precomp.hpp"
#include <float.h>
#include <limits.h>

/* Valery Mosyagin */

//#define TRACKLEVMAR

typedef void (*pointer_LMJac)( const CvMat* src, CvMat* dst );
typedef void (*pointer_LMFunc)( const CvMat* src, CvMat* dst );

/* Optimization using Levenberg-Marquardt */
void cvLevenbergMarquardtOptimization(pointer_LMJac JacobianFunction,
									  pointer_LMFunc function,
									  /*pointer_Err error_function,*/
									  CvMat* X0, CvMat* observRes, CvMat* resultX,
									  int maxIter, double epsilon) {
	/* This is not sparce method */
	/* Make optimization using  */
	/* func - function to compute */
	/* uses function to compute jacobian */

	/* Allocate memory */
	CvMat* vectX = 0;
	CvMat* vectNewX = 0;
	CvMat* resFunc = 0;
	CvMat* resNewFunc = 0;
	CvMat* error = 0;
	CvMat* errorNew = 0;
	CvMat* Jac = 0;
	CvMat* delta = 0;
	CvMat* matrJtJ = 0;
	CvMat* matrJtJN = 0;
	CvMat* matrJt = 0;
	CvMat* vectB = 0;

	CV_FUNCNAME( "cvLevenbegrMarquardtOptimization" );
	__BEGIN__;


	if ( JacobianFunction == 0 || function == 0 || X0 == 0 || observRes == 0 || resultX == 0 ) {
		CV_ERROR( CV_StsNullPtr, "Some of parameters is a NULL pointer" );
	}

	if ( !CV_IS_MAT(X0) || !CV_IS_MAT(observRes) || !CV_IS_MAT(resultX) ) {
		CV_ERROR( CV_StsUnsupportedFormat, "Some of input parameters must be a matrices" );
	}


	int numVal;
	int numFunc;
	double valError;
	double valNewError;

	numVal = X0->rows;
	numFunc = observRes->rows;

	/* test input data */
	if ( X0->cols != 1 ) {
		CV_ERROR( CV_StsUnmatchedSizes, "Number of colomn of vector X0 must be 1" );
	}

	if ( observRes->cols != 1 ) {
		CV_ERROR( CV_StsUnmatchedSizes, "Number of colomn of vector observed rusult must be 1" );
	}

	if ( resultX->cols != 1 || resultX->rows != numVal ) {
		CV_ERROR( CV_StsUnmatchedSizes, "Size of result vector X must be equals to X0" );
	}

	if ( maxIter <= 0  ) {
		CV_ERROR( CV_StsUnmatchedSizes, "Number of maximum iteration must be > 0" );
	}

	if ( epsilon < 0 ) {
		CV_ERROR( CV_StsUnmatchedSizes, "Epsilon must be >= 0" );
	}

	/* copy x0 to current value of x */
	CV_CALL( vectX      = cvCreateMat(numVal, 1,      CV_64F) );
	CV_CALL( vectNewX   = cvCreateMat(numVal, 1,      CV_64F) );
	CV_CALL( resFunc    = cvCreateMat(numFunc, 1,      CV_64F) );
	CV_CALL( resNewFunc = cvCreateMat(numFunc, 1,      CV_64F) );
	CV_CALL( error      = cvCreateMat(numFunc, 1,      CV_64F) );
	CV_CALL( errorNew   = cvCreateMat(numFunc, 1,      CV_64F) );
	CV_CALL( Jac        = cvCreateMat(numFunc, numVal, CV_64F) );
	CV_CALL( delta      = cvCreateMat(numVal, 1,      CV_64F) );
	CV_CALL( matrJtJ    = cvCreateMat(numVal, numVal, CV_64F) );
	CV_CALL( matrJtJN   = cvCreateMat(numVal, numVal, CV_64F) );
	CV_CALL( matrJt     = cvCreateMat(numVal, numFunc, CV_64F) );
	CV_CALL( vectB      = cvCreateMat(numVal, 1,      CV_64F) );

	cvCopy(X0, vectX);

	/* ========== Main optimization loop ============ */
	double change;
	int currIter;
	double alpha;

	change = 1;
	currIter = 0;
	alpha = 0.001;

	do {

		/* Compute value of function */
		function(vectX, resFunc);
		/* Print result of function to file */

		/* Compute error */
		cvSub(observRes, resFunc, error);

		//valError = error_function(observRes,resFunc);
		/* Need to use new version of computing error (norm) */
		valError = cvNorm(observRes, resFunc);

		/* Compute Jacobian for given point vectX */
		JacobianFunction(vectX, Jac);

		/* Define optimal delta for J'*J*delta=J'*error */
		/* compute J'J */
		cvMulTransposed(Jac, matrJtJ, 1);

		cvCopy(matrJtJ, matrJtJN);

		/* compute J'*error */
		cvTranspose(Jac, matrJt);
		cvmMul(matrJt, error, vectB);


		/* Solve normal equation for given alpha and Jacobian */
		do {
			/* Increase diagonal elements by alpha */
			for ( int i = 0; i < numVal; i++ ) {
				double val;
				val = cvmGet(matrJtJ, i, i);
				cvmSet(matrJtJN, i, i, (1 + alpha)*val);
			}

			/* Solve system to define delta */
			cvSolve(matrJtJN, vectB, delta, CV_SVD);

			/* We know delta and we can define new value of vector X */
			cvAdd(vectX, delta, vectNewX);

			/* Compute result of function for new vector X */
			function(vectNewX, resNewFunc);
			cvSub(observRes, resNewFunc, errorNew);

			valNewError = cvNorm(observRes, resNewFunc);

			currIter++;

			if ( valNewError < valError ) {
				/* accept new value */
				valError = valNewError;

				/* Compute relative change of required parameter vectorX. change = norm(curr-prev) / norm(curr) )  */
				change = cvNorm(vectX, vectNewX, CV_RELATIVE_L2);

				alpha /= 10;
				cvCopy(vectNewX, vectX);
				break;
			} else {
				alpha *= 10;
			}

		} while ( currIter < maxIter  );
		/* new value of X and alpha were accepted */

	} while ( change > epsilon && currIter < maxIter );


	/* result was computed */
	cvCopy(vectX, resultX);

	__END__;

	cvReleaseMat(&vectX);
	cvReleaseMat(&vectNewX);
	cvReleaseMat(&resFunc);
	cvReleaseMat(&resNewFunc);
	cvReleaseMat(&error);
	cvReleaseMat(&errorNew);
	cvReleaseMat(&Jac);
	cvReleaseMat(&delta);
	cvReleaseMat(&matrJtJ);
	cvReleaseMat(&matrJtJN);
	cvReleaseMat(&matrJt);
	cvReleaseMat(&vectB);

	return;
}

/*------------------------------------------------------------------------------*/
#if 0
//tests
void Jac_Func2(CvMat* vectX, CvMat* Jac) {
	double x = cvmGet(vectX, 0, 0);
	double y = cvmGet(vectX, 1, 0);
	cvmSet(Jac, 0, 0, 2 * (x - 2));
	cvmSet(Jac, 0, 1, 2 * (y + 3));

	cvmSet(Jac, 1, 0, 1);
	cvmSet(Jac, 1, 1, 1);
	return;
}

void Res_Func2(CvMat* vectX, CvMat* res) {
	double x = cvmGet(vectX, 0, 0);
	double y = cvmGet(vectX, 1, 0);
	cvmSet(res, 0, 0, (x - 2)*(x - 2) + (y + 3)*(y + 3));
	cvmSet(res, 1, 0, x + y);

	return;
}


double Err_Func2(CvMat* obs, CvMat* res) {
	CvMat* tmp;
	tmp = cvCreateMat(obs->rows, 1, CV_64F);
	cvSub(obs, res, tmp);

	double e;
	e = cvNorm(tmp);

	return e;
}


void TestOptimX2Y2() {
	CvMat vectX0;
	double vectX0_dat[2];
	vectX0 = cvMat(2, 1, CV_64F, vectX0_dat);
	vectX0_dat[0] = 5;
	vectX0_dat[1] = -7;

	CvMat observRes;
	double observRes_dat[2];
	observRes = cvMat(2, 1, CV_64F, observRes_dat);
	observRes_dat[0] = 0;
	observRes_dat[1] = -1;
	observRes_dat[0] = 0;
	observRes_dat[1] = -1.2;

	CvMat optimX;
	double optimX_dat[2];
	optimX = cvMat(2, 1, CV_64F, optimX_dat);


	LevenbegrMarquardtOptimization( Jac_Func2, Res_Func2, Err_Func2,
									&vectX0, &observRes, &optimX, 100, 0.000001);

	return;

}

#endif



